Research And Design Of Image Semantic Segmentation Based On Convolutional Neural Network

As an important part of the construction of artificial intelligence vision systems,the field of computer vision has been widely concerned by researchers.Image semantic segmentation,as a key technology for image understanding in the field of computer vision,divides the image by assigning a semantic category label to each pixel in the image.Its research results have been applied to many fields,including in automotive autonomous driving.Environmental scene segmentation,organ image segmentation in medical detection,and remote sensing image segmentation in national security.In recent years,the large-scale application of deep learning has made the Deep Convolutional Neural Network(DCNN)a great success in the field of computer vision.However,image semantic segmentation methods based on convolutional neural networks still exist problems with missing details and context information.Based on the research and analysis of existing convolutional neural network image semantic segmentation methods,and considering the contribution of dilated convolution in improving the receptive field of the network.Based on the encoder-decoder structure and conditional random field post-processing,this paper proposes an improved image Semantic segmentation method.First,the encoder uses dense hollow space pyramid pooling to obtain multi-scale context feature information on a larger receptive field to capture high-level semantic information.Then,the decoder uses jump connections to fuse low-level pixel detail information and high-level semantic information,and uses dense up-sampling convolution to accurately recover the segmentation boundary for the obtained information.Finally,the fully connected conditional random field is used as a post-processing algorithm to further improve the edge segmentation accuracy of the image,so that the overall image segmentation effect is enhanced.In order to verify the effectiveness of the improved image semantic segmentation method designed in this paper,the improved algorithm proposed in this paper is compared with the current most advanced image semantic segmentation algorithm model on the benchmark dataset PASCAL VOC 2012.Experimental data shows that compared with the other three segmentation methods,the proposed method has achieved the best results on three recognized evaluation indexes of image semantic segmentation,and has some advantages in pixel recognition accuracy and object boundary segmentation accuracy promotion.The experimental results strongly prove that the improved method proposed in this paper can not only capture the multi-scale high-level semantic information of the image,but also effectively use the low-level detailed information of the image.The segmentation method based on encoder-decoder structure and conditional random field designed in this paper effectively improves the accuracy of image segmentation.